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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Peter A. Flach,Tijl Bie,Nello Cristianini Conference proceeding

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發(fā)表于 2025-3-21 18:28:28 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning and Knowledge Discovery in Databases
副標題European Conference,
編輯Peter A. Flach,Tijl Bie,Nello Cristianini
視頻videohttp://file.papertrans.cn/621/620490/620490.mp4
概述Up to date results.Fast track conference proceedings.State of the art research
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Peter A. Flach,Tijl Bie,Nello Cristianini Conference proceeding
描述This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and s
出版日期Conference proceedings 2012
關鍵詞Bayesian network classifiers; data mining; hypergraphs; social media; tree search
版次1
doihttps://doi.org/10.1007/978-3-642-33460-3
isbn_softcover978-3-642-33459-7
isbn_ebook978-3-642-33460-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2012
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620490.jpg
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Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methodshis leads to fast algorithms that are applicable to large-scale problems. We empirically analyze the performances of these tree-based learners combined or not with the feature generation capability on several standard datasets.
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Discovering Descriptive Tile Treesthis paper is that we find the optimal tile in only Θ(. min(.,.)) time. Stijl can either be employed as a top-. miner, or by MDL we can identify the tree that describes the data best..Experiments show we find succinct models that accurately summarise the data, and, by their hierarchical property are easily interpretable.
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